# P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds

arXiv: https://arxiv.org/abs/2307.14726 · HTML: https://mlsanity.com/paper/2307.14726/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 28 of 28)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis](https://mlsanity.com/paper/1612.00101/) (2016)
- [SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer](https://mlsanity.com/paper/2108.04444/) (2021)
- [Masked Discrimination for Self-Supervised Learning on Point Clouds](https://mlsanity.com/paper/2203.11183/) (2022)
- [Variational Relational Point Completion Network](https://mlsanity.com/paper/2104.10154/) (2021)
- [High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference](https://mlsanity.com/paper/1709.07599/) (2017)
- [Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion](https://mlsanity.com/paper/2103.07074/) (2021)
- [Unsupervised 3D Shape Completion through GAN Inversion](https://mlsanity.com/paper/2104.13366/) (2021)
- [Unpaired Point Cloud Completion on Real Scans using Adversarial Training](https://mlsanity.com/paper/1904.00069/) (2019)
- [Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding](https://mlsanity.com/paper/2103.07838/) (2021)
- [Dense-Resolution Network for Point Cloud Classification and Segmentation](https://mlsanity.com/paper/2005.06734/) (2020)
- [Weakly-supervised 3D Shape Completion in the Wild](https://mlsanity.com/paper/2008.09110/) (2020)
- [PU-Transformer: Point Cloud Upsampling Transformer](https://mlsanity.com/paper/2111.12242/) (2021)
- [SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering](https://mlsanity.com/paper/2108.00454/) (2021)
- [Learning a Structured Latent Space for Unsupervised Point Cloud Completion](https://mlsanity.com/paper/2203.15580/) (2022)
- [Self-Supervised Point Cloud Completion via Inpainting](https://mlsanity.com/paper/2111.10701/) (2021)
- [PnP-3D: A Plug-and-Play for 3D Point Clouds](https://mlsanity.com/paper/2108.07378/) (2021)
- [Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion](https://mlsanity.com/paper/2211.06820/) (2022)
- [ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion](https://mlsanity.com/paper/2303.01979/) (2023)
- [Self-supervised Point Cloud Completion on Real Traffic Scenes via Scene-concerned Bottom-up Mechanism](https://mlsanity.com/paper/2203.10569/) (2022)
- [A survey on Self Supervised learning approaches for improving Multimodal representation learning](https://mlsanity.com/paper/2210.11024/) (2022)

## Built on by (most cited first, 20 of 20)

- [NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation](https://mlsanity.com/paper/2403.18241/) (2024)
- [Explicitly Guided Information Interaction Network for Cross-modal Point Cloud Completion](https://mlsanity.com/paper/2407.02887/) (2024)
- [GenPC: Zero-shot Point Cloud Completion via 3D Generative Priors](https://mlsanity.com/paper/2502.19896/) (2025)
- [LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency](https://mlsanity.com/paper/2602.18735/) (2026)
- [LAM3D: Large Image-Point-Cloud Alignment Model for 3D Reconstruction from Single Image](https://mlsanity.com/paper/2405.15622/) (2024)
- [Unsupervised 3D Point Cloud Completion via Multi-view Adversarial Learning](https://mlsanity.com/paper/2407.09786/) (2024)
- [DiffPMAE: Diffusion Masked Autoencoders for Point Cloud Reconstruction](https://mlsanity.com/paper/2312.03298/) (2023)
- [Deep Learning for 3D Point Cloud Enhancement: A Survey](https://mlsanity.com/paper/2411.00857/) (2024)
- [Revisiting Point Cloud Completion: Are We Ready For The Real-World?](https://mlsanity.com/paper/2411.17580/) (2024)
- [Self-supervised Shape Completion via Involution and Implicit Correspondences](https://mlsanity.com/paper/2409.15939/) (2024)
- [SPAC-Net: Rethinking Point Cloud Completion with Structural Prior](https://mlsanity.com/paper/2411.15066/) (2024)
- [NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds](https://mlsanity.com/paper/2411.17392/) (2024)
- [DuInNet: Dual-Modality Feature Interaction for Point Cloud Completion](https://mlsanity.com/paper/2407.07374/) (2024)
- [Self-Supervised Large Scale Point Cloud Completion for Archaeological Site Restoration](https://mlsanity.com/paper/2503.04030/) (2025)
- [RealDiff: Real-world 3D Shape Completion using Self-Supervised Diffusion Models](https://mlsanity.com/paper/2409.10180/) (2024)
- [RefComp: A Reference-guided Unified Framework for Unpaired Point Cloud Completion](https://mlsanity.com/paper/2504.13788/) (2025)
- [Denoising-While-Completing Network (DWCNet): Robust Point Cloud Completion Under Corruption](https://mlsanity.com/paper/2507.16743/) (2025)
- [PVNet: Point-Voxel Interaction LiDAR Scene Upsampling Via Diffusion Models](https://mlsanity.com/paper/2508.17050/) (2025)
- [CasPoinTr: Point Cloud Completion with Cascaded Networks and Knowledge Distillation](https://mlsanity.com/paper/2509.23375/) (2025)
- [R1A-PC: Physics-Guided Electromagnetic Inversion of Three-Dimensional Human Point Clouds in Complex Static Environments](https://mlsanity.com/paper/2610.05144/) (2026)

## Abstract (arXiv)

Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of the same object for learning. In contrast to previous approaches, we present Partial2Complete (P2C), the first self-supervised framework that completes point cloud objects using training samples consisting of only a single incomplete point cloud per object. Specifically, our framework groups incomplete point clouds into local patches as input and predicts masked patches by learning prior information from different partial objects. We also propose Region-Aware Chamfer Distance to regularize shape mismatch without limiting completion capability, and devise the Normal Consistency Constraint to incorporate a local planarity assumption, encouraging the recovered shape surface to be continuous and complete. In this way, P2C no longer needs multiple observations or complete point clouds as ground truth. Instead, structural cues are learned from a category-specific dataset to complete partial point clouds of objects. We demonstrate the effectiveness of our approach on both synthetic ShapeNet data and real-world ScanNet data, showing that P2C produces comparable results to methods trained with complete shapes, and outperforms methods learned with multiple partial observations. Code is available at https://github.com/CuiRuikai/Partial2Complete.